Chapter 8 Beta diversity
beta_q0n <- genome_counts_filt %>%
column_to_rownames(., "genome") %>%
filter(rowSums(. != 0, na.rm = TRUE) > 0) %>%
select_if(~!all(. == 0)) %>%
hillpair(., q = 0)
beta_q1n <- genome_counts_filt %>%
column_to_rownames(., "genome") %>%
filter(rowSums(. != 0, na.rm = TRUE) > 0) %>%
select_if(~!all(. == 0)) %>%
hillpair(., q = 1)
beta_q1p <- genome_counts_filt %>%
column_to_rownames(., "genome") %>%
filter(rowSums(. != 0, na.rm = TRUE) > 0) %>%
select_if(~!all(. == 0)) %>%
hillpair(., q = 1, tree = genome_tree)
genome_counts_filt <- genome_counts_filt[genome_counts_filt$genome %in% rownames(genome_gifts),]
rownames(genome_counts_filt) <- NULL
genome_gifts <- genome_gifts[rownames(genome_gifts) %in% genome_counts_filt$genome,]
dist <- genome_gifts %>%
to.elements(., GIFT_db) %>%
traits2dist(., method = "gower")
beta_q1f <- genome_counts_filt %>%
column_to_rownames(., "genome") %>%
filter(rowSums(. != 0, na.rm = TRUE) > 0) %>%
select_if(~!all(. == 0)) %>%
hillpair(., q = 1, dist = dist)8.1 Permanova
Permutation test for homogeneity of multivariate dispersions
Permutation: free
Number of permutations: 999
Response: Distances
Df Sum Sq Mean Sq F N.Perm Pr(>F)
Groups 1 0.09234 0.092344 14.312 999 0.001 ***
Residuals 56 0.36132 0.006452
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Pairwise comparisons:
(Observed p-value below diagonal, permuted p-value above diagonal)
Daneborg Ittoqqortoormii
Daneborg 0.001
Ittoqqortoormii 0.00037875 adonis2(beta_q0n$S ~ region,
data = sample_metadata %>% arrange(match(sample,labels(beta_q1n$C))),
permutations = 999) %>%
broom::tidy() %>%
tt()| term | df | SumOfSqs | R2 | statistic | p.value |
|---|---|---|---|---|---|
| region | 1 | 1.700798 | 0.2101443 | 14.89903 | 0.001 |
| Residual | 56 | 6.392678 | 0.7898557 | NA | NA |
| Total | 57 | 8.093476 | 1.0000000 | NA | NA |
Permutation test for homogeneity of multivariate dispersions
Permutation: free
Number of permutations: 999
Response: Distances
Df Sum Sq Mean Sq F N.Perm Pr(>F)
Groups 1 0.39850 0.39850 26.96 999 0.001 ***
Residuals 56 0.82775 0.01478
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Pairwise comparisons:
(Observed p-value below diagonal, permuted p-value above diagonal)
Daneborg Ittoqqortoormii
Daneborg 0.001
Ittoqqortoormii 2.9964e-06 adonis2(beta_q1n$S ~ region,
data = sample_metadata %>% arrange(match(sample,labels(beta_q1n$C))),
permutations = 999) %>%
broom::tidy() %>%
tt()| term | df | SumOfSqs | R2 | statistic | p.value |
|---|---|---|---|---|---|
| region | 1 | 2.376111 | 0.2881769 | 22.67123 | 0.001 |
| Residual | 56 | 5.869211 | 0.7118231 | NA | NA |
| Total | 57 | 8.245323 | 1.0000000 | NA | NA |
#Phylogenetic diversity
betadisper(beta_q1p$S, sample_metadata$region) %>% permutest(., pairwise = TRUE)
Permutation test for homogeneity of multivariate dispersions
Permutation: free
Number of permutations: 999
Response: Distances
Df Sum Sq Mean Sq F N.Perm Pr(>F)
Groups 1 0.10517 0.105171 12.067 999 0.001 ***
Residuals 56 0.48808 0.008716
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Pairwise comparisons:
(Observed p-value below diagonal, permuted p-value above diagonal)
Daneborg Ittoqqortoormii
Daneborg 0.002
Ittoqqortoormii 0.00099752 adonis2(beta_q1p$S ~ region,
data = sample_metadata %>% arrange(match(sample,labels(beta_q1n$C))),
permutations = 999) %>%
broom::tidy() %>%
tt()| term | df | SumOfSqs | R2 | statistic | p.value |
|---|---|---|---|---|---|
| region | 1 | 0.1387067 | 0.101621 | 6.334495 | 0.001 |
| Residual | 56 | 1.2262339 | 0.898379 | NA | NA |
| Total | 57 | 1.3649405 | 1.000000 | NA | NA |
#Functional diversity
betadisper(beta_q1f$S, sample_metadata$region) %>% permutest(., pairwise = TRUE)
Permutation test for homogeneity of multivariate dispersions
Permutation: free
Number of permutations: 999
Response: Distances
Df Sum Sq Mean Sq F N.Perm Pr(>F)
Groups 1 0.003452 0.0034517 0.8969 999 0.353
Residuals 56 0.215512 0.0038484
Pairwise comparisons:
(Observed p-value below diagonal, permuted p-value above diagonal)
Daneborg Ittoqqortoormii
Daneborg 0.355
Ittoqqortoormii 0.34768 adonis2(beta_q1f$S ~ region,
data = sample_metadata %>% arrange(match(sample,labels(beta_q1n$C))),
permutations = 999) %>%
broom::tidy() %>%
tt()| term | df | SumOfSqs | R2 | statistic | p.value |
|---|---|---|---|---|---|
| region | 1 | 0.01958829 | 0.05063651 | 2.98689 | 0.177 |
| Residual | 56 | 0.36725289 | 0.94936349 | NA | NA |
| Total | 57 | 0.38684118 | 1.00000000 | NA | NA |
8.2 Plot
8.2.1 Richness diversity
beta_q0n$S %>%
vegan::metaMDS(., trymax = 500, k = 2, trace=0) %>%
vegan::scores() %>%
as_tibble(., rownames = "sample") %>%
dplyr::left_join(sample_metadata, by = join_by(sample == sample)) %>%
group_by(region) %>%
mutate(x_cen = mean(NMDS1, na.rm = TRUE)) %>%
mutate(y_cen = mean(NMDS2, na.rm = TRUE)) %>%
ungroup() %>%
ggplot(aes(x = NMDS1, y = NMDS2, color = region, fill = region)) +
scale_color_manual(name="Region",
values=c("#6A9AC3","#F3B942")) +
scale_fill_manual(name="Region",
values=c("#6A9AC350","#F3B94250")) +
geom_point(size = 4) +
# stat_ellipse(aes(color = beta_q1n_nmds$Groups))+
geom_segment(aes(x = x_cen, y = y_cen, xend = NMDS1, yend = NMDS2), alpha = 0.9, show.legend = FALSE) +
theme_classic() +
theme(
axis.text.x = element_text(size = 12),
axis.text.y = element_text(size = 12),
axis.title = element_text(size = 20, face = "bold"),
axis.text = element_text(face = "bold", size = 18),
panel.background = element_blank(),
axis.line = element_line(size = 0.5, linetype = "solid", colour = "black"),
legend.text = element_text(size = 16),
legend.title = element_text(size = 18),
legend.position = "right", legend.box = "vertical"
)
8.2.2 Neutral diversity
beta_q1n$S %>%
vegan::metaMDS(., trymax = 500, k = 2, trace=0) %>%
vegan::scores() %>%
as_tibble(., rownames = "sample") %>%
dplyr::left_join(sample_metadata, by = join_by(sample == sample)) %>%
group_by(region) %>%
mutate(x_cen = mean(NMDS1, na.rm = TRUE)) %>%
mutate(y_cen = mean(NMDS2, na.rm = TRUE)) %>%
ungroup() %>%
ggplot(aes(x = NMDS1, y = NMDS2, color = region, fill = region)) +
scale_color_manual(name="Region",
values=c("#6A9AC3","#F3B942")) +
scale_fill_manual(name="Region",
values=c("#6A9AC350","#F3B94250")) +
geom_point(size = 4) +
geom_segment(aes(x = x_cen, y = y_cen, xend = NMDS1, yend = NMDS2), alpha = 0.9) +
theme_classic() +
theme(
axis.text.x = element_text(size = 12),
axis.text.y = element_text(size = 12),
axis.title = element_text(size = 20, face = "bold"),
axis.text = element_text(face = "bold", size = 18),
panel.background = element_blank(),
axis.line = element_line(size = 0.5, linetype = "solid", colour = "black"),
legend.text = element_text(size = 16),
legend.title = element_text(size = 18),
legend.position = "right", legend.box = "vertical"
)
8.2.3 Phylogenetic diversity
beta_q1p$S %>%
vegan::metaMDS(., trymax = 500, k = 2, trace=0) %>%
vegan::scores() %>%
as_tibble(., rownames = "sample") %>%
dplyr::left_join(sample_metadata, by = join_by(sample == sample)) %>%
group_by(region) %>%
mutate(x_cen = mean(NMDS1, na.rm = TRUE)) %>%
mutate(y_cen = mean(NMDS2, na.rm = TRUE)) %>%
ungroup() %>%
ggplot(aes(x = NMDS1, y = NMDS2, color = region, fill = region)) +
scale_color_manual(name="Region",
values=c("#6A9AC3","#F3B942")) +
scale_fill_manual(name="Region",
values=c("#6A9AC350","#F3B94250")) +
geom_point(size = 4) +
geom_segment(aes(x = x_cen, y = y_cen, xend = NMDS1, yend = NMDS2), alpha = 0.9) +
theme_classic() +
theme(
axis.text.x = element_text(size = 12),
axis.text.y = element_text(size = 12),
axis.title = element_text(size = 20, face = "bold"),
axis.text = element_text(face = "bold", size = 18),
panel.background = element_blank(),
axis.line = element_line(size = 0.5, linetype = "solid", colour = "black"),
legend.text = element_text(size = 16),
legend.title = element_text(size = 18),
legend.position = "right", legend.box = "vertical"
)
8.2.4 Functional diversity
beta_q1f$S %>%
vegan::metaMDS(., trymax = 500, k = 2, trace=0) %>%
vegan::scores() %>%
as_tibble(., rownames = "sample") %>%
dplyr::left_join(sample_metadata, by = join_by(sample == sample)) %>%
group_by(region) %>%
mutate(x_cen = mean(NMDS1, na.rm = TRUE)) %>%
mutate(y_cen = mean(NMDS2, na.rm = TRUE)) %>%
ungroup() %>%
ggplot(aes(x = NMDS1, y = NMDS2, color = region, fill = region)) +
scale_color_manual(name="Region",
values=c("#6A9AC3","#F3B942")) +
scale_fill_manual(name="Region",
values=c("#6A9AC350","#F3B94250")) +
geom_point(size = 4) +
geom_segment(aes(x = x_cen, y = y_cen, xend = NMDS1, yend = NMDS2), alpha = 0.9) +
theme_classic() +
theme(
axis.text.x = element_text(size = 12),
axis.text.y = element_text(size = 12),
axis.title = element_text(size = 20, face = "bold"),
axis.text = element_text(face = "bold", size = 18),
panel.background = element_blank(),
axis.line = element_line(size = 0.5, linetype = "solid", colour = "black"),
legend.text = element_text(size = 16),
legend.title = element_text(size = 18),
legend.position = "right", legend.box = "vertical"
)